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Measuring the match between evaluators and evaluees: cognitive distances between panel members and research groups at the journal level


When research groups are evaluated by an expert panel, it is an open question how one can determine the match between panel and research groups. In this paper, we outline two quantitative approaches that determine the cognitive distance between evaluators and evaluees, based on the journals they have published in. We use example data from four research evaluations carried out between 2009 and 2014 at the University of Antwerp.

While the barycenter approach is based on a journal map, the similarity-adapted publication vector (SAPV) approach is based on the full journal similarity matrix. Both approaches determine an entity’s profile based on the journals in which it has published. Subsequently, we determine the Euclidean distance between the barycenter or SAPV profiles of two entities as an indicator of the cognitive distance between them. Using a bootstrapping approach, we determine confidence intervals for these distances. As such, the present article constitutes a refinement of a previous proposal that operates on the level of Web of Science subject categories.

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  1. 1.

    The Science and Social Science Editions 2011 contain 8281 and 2943 journals respectively. Of these journals, 549 are contained in both databases.

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  1. Abramo, G., & D’Angelo, C. A. (2011). Evaluating research: From informed peer review to bibliometrics. Scientometrics, 87(3), 499–514.

  2. Barker, K. (2007). The UK research assessment exercise: The evolution of a national research evaluation system. Research Evaluation, 16(1), 3–12. doi:10.3152/095820207X190674.

  3. Berendsen, R., de Rijke, M., Balog, K., Bogers, T., & Bosch, A. (2013). On the assessment of expertise profiles. Journal of the American Society for Information Science and Technology, 64(10), 2024–2044. doi:10.1002/asi.22908.

  4. Bornmann, L., Mutz, R., Marx, W., Schier, H., & Daniel, H.-D. (2011). A multilevel modelling approach to investigating the predictive validity of editorial decisions: Do the editors of a high profile journal select manuscripts that are highly cited after publication? Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(4), 857–879. doi:10.1111/j.1467-985X.2011.00689.x.

  5. Borum, F., & Hansen, H. F. (2000). The local construction and enactment of standards for research evaluation: The case of the Copenhagen Business School. Evaluation, 6(3), 281–299. doi:10.1177/13563890022209299.

  6. Boyack, K. W., Chen, M.-C., & Chacko, G. (2014). Characterization of the peer review network at the center for scientific review, National Institutes of Health. PLoS ONE, 9(8), e104244. doi:10.1371/journal.pone.0104244.

  7. Boyack, K. W., & Klavans, R. (2014). Creation of a highly detailed, dynamic, global model and map of science. Journal of the Association for Information Science and Technology, 65(4), 670–685. doi:10.1002/asi.22990.

  8. Buckley, H. L., Sciligo, A. R., Adair, K. L., Case, B. S., & Monks, J. M. (2014). Is there gender bias in reviewer selection and publication success rates for the New Zealand Journal of Ecology? New Zealand Journal of Ecology, 38(2), 335–339.

  9. Butler, L., & McAllister, I. (2011). Evaluating university research performance using metrics. European Political Science, 10(1), 44–58. doi:10.1057/eps.2010.13.

  10. Chen, S., Arsenault, C., Gingras, Y., & Lariviere, V. (2015). Exploring the interdisciplinary evolution of a discipline: The case of biochemistry and molecular biology. Scientometrics, 102(2), 1307–1323. doi:10.1007/s11192-014-1457-6.

  11. Cohen, W. M., & Levinthal, D. A. (1989). Innovation and learning: The two faces of R&D. The Economic Journal, 99(397), 569–596. doi:10.2307/2233763.

  12. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. doi:10.2307/2393553.

  13. Coryn, C. L. S., & Scriven, M. (2008). Editor’s notes. In C. L. S. Coryn & M. Scriven (Eds.), Reforming the evaluation of research: New directions for evaluation (Vol. 118, pp. 1–5). California: American Evaluation Association.

  14. Efron, B., & Tibshirani, R. J. (1998). An introduction to the bootstrap. Boca Raton, FL: Chapman & Hall/CRC.

  15. Egghe, L., & Rousseau, R. (1990). Introduction to informetrics. Elsevier. Retrieved from

  16. Engels, T. C. E., Goos, P., Dexters, N., & Spruyt, E. H. J. (2013). Group size, h-index, and efficiency in publishing in top journals explain expert panel assessments of research group quality and productivity. Research Evaluation, 22(4), 224–236. doi:10.1093/reseval/rvt013.

  17. Engels, T. C. E., Ossenblok, T. L. B., & Spruyt, E. H. J. (2012). Changing publication patterns in the social sciences and humanities, 2000–2009. Scientometrics, 93(2), 373–390.

  18. ESF. (2011). European peer review guide: Integrating policies and practices into coherent procedures. Strasbourg: European Science Foundation.

  19. Fields, C. (2015). How small is the center of science? Short cross-disciplinary cycles in co-authorship graphs. Scientometrics, 102(2), 1287–1306. doi:10.1007/s11192-014-1468-3.

  20. Gorjiara, T., & Baldock, C. (2014). Nanoscience and nanotechnology research publications: A comparison between Australia and the rest of the world. Scientometrics, 100(1), 121–148. doi:10.1007/s11192-014-1287-6.

  21. Gould, T. H. P. (2013). Do we still need peer review? An argument for change (Vol. 65). Plymouth: Scarecrow Press.

  22. Grauwin, S., & Jensen, P. (2011). Mapping scientific institutions. Scientometrics, 89(3), 943–954. doi:10.1007/s11192-011-0482-y.

  23. Hansson, F. (2010). Dialogue in or with the peer review? Evaluating research organizations in order to promote organizational learning. Science and Public Policy, 37(4), 239–251. doi:10.3152/030234210X496600.

  24. Hashemi, S. H., Neshati, M., & Beigy, H. (2013). Expertise retrieval in bibliographic network: A topic dominance learning approach. In Proceedings of the 22nd ACM international conference on information & knowledge management (pp. 1117–1126). San Francisco, US: ACM. doi:10.1145/2505515.2505697.

  25. Hofmann, K., Balog, K., Bogers, T., & de Rijke, M. (2010). Contextual factors for finding similar experts. Journal of the American Society for Information Science and Technology, 61(5), 994–1014. doi:10.1002/asi.21292.

  26. Jin, B., & Rousseau, R. (2001). An introduction to the barycentre method with an application to China’s mean centre of publication. Libri, 51(4), 225–233. doi:10.1515/LIBR.2001.225.

  27. Kamada, T., & Kawai, S. (1989). An algorithm for drawing general undirected graphs. Information Processing Letters, 31(1), 7–15. doi:10.1016/0020-0190(89)90102-6.

  28. Kington, J. (2014). Balanced cross sections, shortening estimates, and the magnitude of out-of-sequence thrusting in the Nankai Trough accretionary prism. Japan: Figshare. doi:10.6084/m9.figshare.1015774.v1.

  29. Lawrenz, F., Thao, M., & Johnson, K. (2012). Expert panel reviews of research centers: The site visit process. Evaluation and Program Planning, 35(3), 390–397. doi:10.1016/j.evalprogplan.2012.01.003.

  30. Lee, C. J., Sugimoto, C. R., Zhang, G., & Cronin, B. (2013). Bias in peer review. Journal of the American Society for Information Science and Technology, 64(1), 2–17. doi:10.1002/asi.22784.

  31. Leydesdorff, L., & de Nooy, W. (2015). Can “Hot Spots” in the sciences be mapped using the dynamics of aggregated journal-journal citation relations? Retrieved from

  32. Leydesdorff, L., Heimeriks, G., & Rotolo, D. (2015). Journal portfolio analysis for countries, cities, and organizations: Maps and comparisons. Journal of the Association for Information Science and Technology,. doi:10.1002/asi.23551.

  33. Leydesdorff, L., & Rafols, I. (2012). Interactive overlays: A new method for generating global journal maps from web-of-science data. Journal of Informetrics, 6(2), 318–332. doi:10.1016/j.joi.2011.11.003.

  34. Leydesdorff, L., Rafols, I., & Chen, C. (2013). Interactive overlays of journals and the measurement of interdisciplinarity on the basis of aggregated journal–journal citations. Journal of the American Society for Information Science and Technology, 64(12), 2573–2586. doi:10.1002/asi.22946.

  35. Li, D., & Agha, L. (2015). Big names or big ideas: Do peer-review panels select the best science proposals? Science, 348(6233), 434–438. doi:10.1126/science.aaa0185.

  36. McKenna, H. P. (2015). Research assessment: The impact of impact. International Journal of Nursing Studies, 52(1), 1–3. doi:10.1016/j.ijnurstu.2014.11.012.

  37. Milat, A. J., Bauman, A. E., & Redman, S. (2015). A narrative review of research impact assessment models and methods. Health Research Policy and Systems, 13, 18. doi:10.1186/s12961-015-0003-1.

  38. Molas-Gallart, J. (2012). Research governance and the role of evaluation: A comparative study. American Journal of Evaluation, 33(4), 583–598. doi:10.1177/1098214012450938.

  39. Nedeva, M., Georghiou, L., Loveridge, D., & Cameron, H. (1996). The use of co-nomination to identify expert participants for technology foresight. R&D Management, 26(2), 155–168.

  40. Neshati, M., Beigy, H., & Hiemstra, D. (2012). Multi-aspect group formation using facility location analysis. In Proceedings of the seventeenth Australasian document computing symposium (pp. 62–71). New York: ACM. doi:10.1145/2407085.2407094.

  41. Nooteboom, B. (1999). Inter-firm alliances: Analysis and design. London: Routledge.

  42. Nooteboom, B. (2000). Learning by interaction: Absorptive capacity, cognitive distance and governance. Journal of Management and Governance, 4(1–2), 69–92.

  43. Nooteboom, B., Van Haverbeke, W., Duysters, G., Gilsing, V., & van den Oord, A. (2007). Optimal cognitive distance and absorptive capacity. Research Policy, 36(7), 1016–1034. doi:10.1016/j.respol.2007.04.003.

  44. Oleinik, A. (2014). Conflict(s) of interest in peer review: Its origins and possible solutions. Science and Engineering Ethics, 20(1), 55–75. doi:10.1007/s11948-012-9426-z.

  45. Pina, D. G., Hren, D., & Marušić, A. (2015). Peer review evaluation process of Marie Curie actions under EU’s seventh framework programme for research. PLoS ONE, 10(6), e0130753. doi:10.1371/journal.pone.0130753.

  46. Rafols, I., Porter, A. L., & Leydesdorff, L. (2010). Science overlay maps: A new tool for research policy and library management. Journal of the American Society for Information Science and Technology, 61(9), 1871–1887. doi:10.1002/asi.21368.

  47. Rahm, E. (2008). Comparing the scientific impact of conference and journal publications in computer science. Information Services and Use, 28(2), 127–128.

  48. Rahman, A. I. M. J., Guns, R., Rousseau, R., & Engels, T. C. E. (2014). Assessment of expertise overlap between an expert panel and research groups. In E. Noyons (Ed.), Context counts: Pathways to master big and little data. Proceedings of the science and technology indicators conference 2014 Leiden (pp. 295–301). Leiden: Universiteit Leiden.

  49. Rahman, A. I. M. J., Guns, R., Rousseau, R., & Engels, T. C. E. (2015). Is the expertise of evaluation panels congruent with the research interests of the research groups: A quantitative approach based on barycenters. Journal of Informetrics, 9(4), 704–721. doi:10.1016/j.joi.2015.07.009.

  50. Rons, N., De Bruyn, A., & Cornelis, J. (2008). Research evaluation per discipline: A peer-review method and its outcomes. Research Evaluation, 17(1), 45–57. doi:10.3152/095820208X240208.

  51. Rousseau, R. (1989). Kinematical statistics of scientific output. Part I: Geographical approach. Revue Française de Bibliométrie, 4, 50–64.

  52. Rousseau, R. (2008). Triad or tetrad: Another representation. ISSI Newsletter, 4(1), 5–7.

  53. Rousseau, R., Rahman, A. I. M. J., Guns, R., & Engels, T. C. E. (2016). A note and a correction on measuring cognitive distance in multiple dimensions. Retrieved from

  54. Rybak, J., Balog, K., & Nørvåg, K. (2014). ExperTime: Tracking expertise over time. In Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval (pp. 1273–1274). Broadbeach: ACM. doi:10.1145/2600428.2611190.

  55. Simon, D., & Knie, A. (2013). Can evaluation contribute to the organizational development of academic institutions? An international comparison. Evaluation, 19(4), 402–418. doi:10.1177/1356389013505806.

  56. Sobkowicz, P. (2015). Innovation suppression and clique evolution in peer-review-based, competitive research funding systems: An agent-based model. Journal of Artificial Societies and Social Simulation, 18(2), 13.

  57. Tseng, Y. H., & Tsay, M. Y. (2013). Journal clustering of library and information science for subfield delineation using the bibliometric analysis toolkit: CATAR. Scientometrics, 95(2), 503–528. doi:10.1007/s11192-013-0964-1.

  58. van den Besselaar, P., & Leydesdorff, L. (2009). Past performance, peer review and project selection: A case study in the social and behavioral sciences. Research Evaluation, 18(4), 273–288. doi:10.3152/095820209X475360.

  59. van Eck, N. J., & Waltman, L. (2007). VOS: A new method for visualizing similarities between objects. In R. Decker & H.-J. Lenz (Eds.), Advances in data analysis: Proceedings of the 30th annual conference of the German Classification Society advances in data analysis (pp. 299–306). London: Springer.

  60. van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. doi:10.1007/s11192-009-0146-3.

  61. van Eck, N. J., Waltman, L., Dekker, R., & van den Berg, J. (2010). A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS. Journal of the American Society for Information Science and Technology, 61(12), 2405–2416. doi:10.1002/asi.21421.

  62. Verleysen, F. T., & Engels, T. C. E. (2013). Measuring internationalisation of book publishing in the social sciences and humanities using the barycentre method. In J. Gorraiz, E. Schiebel, C. Gumpenberger, M. Horlesberger, & H. Moed (Eds.), Proceedings of the 14th international society of scientometrics and informetrics conference (ISSI), 1519 July 2013 (pp. 1170–1176). Vienna, Austria.

  63. Verleysen, F. T., & Engels, T. C. E. (2014). Barycenter representation of book publishing internationalization in the social sciences and humanities. Journal of Informetrics, 8(1), 234–240. doi:10.1016/j.joi.2013.11.008.

  64. VSNU. (2003). Standard evaluation protocol 2003–2009 for public research organisations. Utrecht/den Haag/Amsterdam: VSNU, NWO and KNAW.

  65. VSNU. (2009). Standard evaluation protocol 2009–2015: Protocol for research assessment in The Netherlands. Utrecht/den Haag/Amsterdam: VSNU, NWO and KNAW.

  66. Waltman, L., & van Eck, N. J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology, 63(12), 2378–2392. doi:10.1002/asi.22748.

  67. Wang, Q., & Sandström, U. (2015). Defining the role of cognitive distance in the peer review process with an explorative study of a grant scheme in infection biology. Research Evaluation, 24(3), 271–281. doi:10.1093/reseval/rvv009.

  68. Wessely, S. (1998). Peer review of grant applications: What do we know? The Lancet, 352(9124), 301–305. doi:10.1016/S0140-6736(97)11129-1.

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The authors thank Ronald Rousseau for stimulating and insightful suggestions related to the topic of the paper and Thomson Reuters for making the journal citation data available. This investigation has been made possible by the financial support of the Flemish government to ECOOM, among others. The opinions in the paper are the authors’ and not necessarily those of the government. We thank the reviewers for their constructive remarks.

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Correspondence to A. I. M. Jakaria Rahman.

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Rahman, A.I.M.J., Guns, R., Leydesdorff, L. et al. Measuring the match between evaluators and evaluees: cognitive distances between panel members and research groups at the journal level. Scientometrics 109, 1639–1663 (2016).

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  • Research evaluation
  • Barycenter
  • Similarity-adapted publication vector
  • Journal overlay map
  • Matching research expertise
  • Similarity matrix